Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations2475
Missing cells10
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.8 KiB
Average record size in memory88.1 B

Variable types

Numeric7
Text3
Categorical1

Alerts

Category is highly overall correlated with Category ID and 2 other fieldsHigh correlation
Category ID is highly overall correlated with Category and 2 other fieldsHigh correlation
Cluster ID is highly overall correlated with Category and 2 other fieldsHigh correlation
Product ID is highly overall correlated with Category and 2 other fieldsHigh correlation
Price is highly skewed (γ1 = 28.64400963) Skewed
Product ID has unique values Unique
Price has 150 (6.1%) zeros Zeros

Reproduction

Analysis started2024-11-12 03:20:41.240774
Analysis finished2024-11-12 03:20:46.926412
Duration5.69 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Product ID
Real number (ℝ)

High correlation  Unique 

Distinct2475
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26449.261
Minimum5
Maximum47341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-11-12T03:20:47.019578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1712.1
Q115046
median27943
Q337893.5
95-th percentile45543.3
Maximum47341
Range47336
Interquartile range (IQR)22847.5

Descriptive statistics

Standard deviation13565.141
Coefficient of variation (CV)0.51287411
Kurtosis-0.97393913
Mean26449.261
Median Absolute Deviation (MAD)11280
Skewness-0.3306697
Sum65461920
Variance1.8401305 × 108
MonotonicityNot monotonic
2024-11-12T03:20:47.153939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43860 1
 
< 0.1%
33762 1
 
< 0.1%
26365 1
 
< 0.1%
17098 1
 
< 0.1%
15388 1
 
< 0.1%
12451 1
 
< 0.1%
16342 1
 
< 0.1%
44930 1
 
< 0.1%
37688 1
 
< 0.1%
30825 1
 
< 0.1%
Other values (2465) 2465
99.6%
ValueCountFrequency (%)
5 1
< 0.1%
14 1
< 0.1%
41 1
< 0.1%
62 1
< 0.1%
71 1
< 0.1%
93 1
< 0.1%
97 1
< 0.1%
120 1
< 0.1%
131 1
< 0.1%
140 1
< 0.1%
ValueCountFrequency (%)
47341 1
< 0.1%
47299 1
< 0.1%
47285 1
< 0.1%
47280 1
< 0.1%
47274 1
< 0.1%
47267 1
< 0.1%
47266 1
< 0.1%
47235 1
< 0.1%
47202 1
< 0.1%
47188 1
< 0.1%
Distinct2457
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size19.5 KiB
2024-11-12T03:20:47.491472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length192
Median length121
Mean length52.662626
Min length9

Characters and Unicode

Total characters130340
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2440 ?
Unique (%)98.6%

Sample

1st rowbosch serie 4 kil22vf30g integrated fridge
2nd rowsony kd75xf8596bu 75 4k hdr ultra hd smart android led tv amazon alexa
3rd rowsony xperia xa2 ultra black 6 32gb 4g unlocked sim free
4th rowpentax k 1 body hd 24 70mm f/2 8 ed sdm wr
5th rowsamsung ue75mu7000txxu 75 inch 7 series led smart tv black/silver
ValueCountFrequency (%)
fridge 460
 
2.2%
freezer 434
 
2.0%
white 388
 
1.8%
in 266
 
1.2%
black 265
 
1.2%
a 243
 
1.1%
free 240
 
1.1%
bosch 220
 
1.0%
tv 201
 
0.9%
processor 185
 
0.9%
Other values (4308) 18399
86.4%
2024-11-12T03:20:48.004633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18826
 
14.4%
e 11407
 
8.8%
r 7515
 
5.8%
i 7038
 
5.4%
s 6111
 
4.7%
a 6084
 
4.7%
t 5073
 
3.9%
n 5042
 
3.9%
l 4635
 
3.6%
o 4498
 
3.5%
Other values (29) 54111
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18826
 
14.4%
e 11407
 
8.8%
r 7515
 
5.8%
i 7038
 
5.4%
s 6111
 
4.7%
a 6084
 
4.7%
t 5073
 
3.9%
n 5042
 
3.9%
l 4635
 
3.6%
o 4498
 
3.5%
Other values (29) 54111
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18826
 
14.4%
e 11407
 
8.8%
r 7515
 
5.8%
i 7038
 
5.4%
s 6111
 
4.7%
a 6084
 
4.7%
t 5073
 
3.9%
n 5042
 
3.9%
l 4635
 
3.6%
o 4498
 
3.5%
Other values (29) 54111
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18826
 
14.4%
e 11407
 
8.8%
r 7515
 
5.8%
i 7038
 
5.4%
s 6111
 
4.7%
a 6084
 
4.7%
t 5073
 
3.9%
n 5042
 
3.9%
l 4635
 
3.6%
o 4498
 
3.5%
Other values (29) 54111
41.5%

Merchant ID
Real number (ℝ)

Distinct182
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.18303
Minimum1
Maximum367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-11-12T03:20:48.139142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q117
median88
Q3250
95-th percentile314
Maximum367
Range366
Interquartile range (IQR)233

Descriptive statistics

Standard deviation116.39138
Coefficient of variation (CV)0.96045936
Kurtosis-1.0860371
Mean121.18303
Median Absolute Deviation (MAD)74
Skewness0.66218284
Sum299928
Variance13546.952
MonotonicityNot monotonic
2024-11-12T03:20:48.283745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 170
 
6.9%
6 125
 
5.1%
298 124
 
5.0%
119 97
 
3.9%
7 96
 
3.9%
17 84
 
3.4%
293 73
 
2.9%
31 66
 
2.7%
294 61
 
2.5%
131 60
 
2.4%
Other values (172) 1519
61.4%
ValueCountFrequency (%)
1 15
 
0.6%
2 33
 
1.3%
3 170
6.9%
4 23
 
0.9%
5 18
 
0.7%
6 125
5.1%
7 96
3.9%
8 17
 
0.7%
9 2
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
367 1
 
< 0.1%
364 5
 
0.2%
359 1
 
< 0.1%
356 1
 
< 0.1%
350 3
 
0.1%
348 1
 
< 0.1%
347 22
0.9%
346 4
 
0.2%
344 2
 
0.1%
343 1
 
< 0.1%

Cluster ID
Real number (ℝ)

High correlation 

Distinct2190
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30377.84
Minimum1
Maximum47505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-11-12T03:20:48.427620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile252.7
Q16154.5
median40693
Q344098.5
95-th percentile46495
Maximum47505
Range47504
Interquartile range (IQR)37944

Descriptive statistics

Standard deviation18373.007
Coefficient of variation (CV)0.60481611
Kurtosis-1.3491776
Mean30377.84
Median Absolute Deviation (MAD)3897
Skewness-0.75140668
Sum75185153
Variance3.3756737 × 108
MonotonicityNot monotonic
2024-11-12T03:20:48.564765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4463 4
 
0.2%
4465 4
 
0.2%
40467 4
 
0.2%
44056 4
 
0.2%
40486 4
 
0.2%
4422 4
 
0.2%
43292 3
 
0.1%
46191 3
 
0.1%
4514 3
 
0.1%
46713 3
 
0.1%
Other values (2180) 2439
98.5%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 2
0.1%
6 2
0.1%
7 2
0.1%
10 2
0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
47505 1
< 0.1%
47459 1
< 0.1%
47445 1
< 0.1%
47440 1
< 0.1%
47434 1
< 0.1%
47427 1
< 0.1%
47426 1
< 0.1%
47395 1
< 0.1%
47362 1
< 0.1%
47348 1
< 0.1%
Distinct2182
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size19.5 KiB
2024-11-12T03:20:48.852286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length78
Median length68
Mean length24.874343
Min length6

Characters and Unicode

Total characters61564
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1926 ?
Unique (%)77.8%

Sample

1st rowBosch KIL22VF30G Integrated
2nd rowSony KD-75XF8596
3rd rowSony Xperia XA2 Ultra 32GB
4th rowPentax K-1 + 24-70mm
5th rowSamsung UE75MU7000
ValueCountFrequency (%)
white 352
 
3.8%
integrated 335
 
3.6%
stainless 240
 
2.6%
steel 240
 
2.6%
bosch 239
 
2.6%
intel 183
 
2.0%
samsung 180
 
1.9%
tray 169
 
1.8%
xeon 122
 
1.3%
black 111
 
1.2%
Other values (2674) 7129
76.7%
2024-11-12T03:20:49.323253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6891
 
11.2%
e 4106
 
6.7%
t 2321
 
3.8%
n 2090
 
3.4%
S 2044
 
3.3%
0 2021
 
3.3%
a 2003
 
3.3%
o 1907
 
3.1%
i 1878
 
3.1%
s 1598
 
2.6%
Other values (62) 34705
56.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6891
 
11.2%
e 4106
 
6.7%
t 2321
 
3.8%
n 2090
 
3.4%
S 2044
 
3.3%
0 2021
 
3.3%
a 2003
 
3.3%
o 1907
 
3.1%
i 1878
 
3.1%
s 1598
 
2.6%
Other values (62) 34705
56.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6891
 
11.2%
e 4106
 
6.7%
t 2321
 
3.8%
n 2090
 
3.4%
S 2044
 
3.3%
0 2021
 
3.3%
a 2003
 
3.3%
o 1907
 
3.1%
i 1878
 
3.1%
s 1598
 
2.6%
Other values (62) 34705
56.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6891
 
11.2%
e 4106
 
6.7%
t 2321
 
3.8%
n 2090
 
3.4%
S 2044
 
3.3%
0 2021
 
3.3%
a 2003
 
3.3%
o 1907
 
3.1%
i 1878
 
3.1%
s 1598
 
2.6%
Other values (62) 34705
56.4%

Category ID
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2618.223
Minimum2612
Maximum2623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-11-12T03:20:49.441410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2612
5-th percentile2612
Q12615
median2619
Q32622
95-th percentile2623
Maximum2623
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6160117
Coefficient of variation (CV)0.0013810938
Kurtosis-1.2040583
Mean2618.223
Median Absolute Deviation (MAD)3
Skewness-0.33347018
Sum6480102
Variance13.075541
MonotonicityNot monotonic
2024-11-12T03:20:49.549231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2622 405
16.4%
2612 278
11.2%
2623 268
10.8%
2615 266
10.7%
2620 266
10.7%
2614 249
10.1%
2619 234
9.5%
2617 186
7.5%
2618 165
6.7%
2621 158
 
6.4%
ValueCountFrequency (%)
2612 278
11.2%
2614 249
10.1%
2615 266
10.7%
2617 186
7.5%
2618 165
6.7%
2619 234
9.5%
2620 266
10.7%
2621 158
 
6.4%
2622 405
16.4%
2623 268
10.8%
ValueCountFrequency (%)
2623 268
10.8%
2622 405
16.4%
2621 158
 
6.4%
2620 266
10.7%
2619 234
9.5%
2618 165
6.7%
2617 186
7.5%
2615 266
10.7%
2614 249
10.1%
2612 278
11.2%

Category
Categorical

High correlation 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size19.5 KiB
Fridge Freezers
405 
Mobile Phones
278 
Fridges
268 
CPUs
266 
Washing Machines
266 
Other values (5)
992 

Length

Max length16
Median length11
Mean length10.468687
Min length3

Characters and Unicode

Total characters25910
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFridges
2nd rowTVs
3rd rowMobile Phones
4th rowDigital Cameras
5th rowTVs

Common Values

ValueCountFrequency (%)
Fridge Freezers 405
16.4%
Mobile Phones 278
11.2%
Fridges 268
10.8%
CPUs 266
10.7%
Washing Machines 266
10.7%
TVs 249
10.1%
Dishwashers 234
9.5%
Digital Cameras 186
7.5%
Microwaves 165
6.7%
Freezers 158
 
6.4%

Length

2024-11-12T03:20:49.668484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T03:20:49.796194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
freezers 563
15.6%
fridge 405
11.2%
mobile 278
7.7%
phones 278
7.7%
fridges 268
7.4%
cpus 266
7.4%
washing 266
7.4%
machines 266
7.4%
tvs 249
6.9%
dishwashers 234
6.5%
Other values (3) 537
14.9%

Most occurring characters

ValueCountFrequency (%)
e 3769
14.5%
s 3209
12.4%
r 2384
 
9.2%
i 2254
 
8.7%
a 1489
 
5.7%
h 1278
 
4.9%
F 1236
 
4.8%
1135
 
4.4%
g 1125
 
4.3%
n 810
 
3.1%
Other values (18) 7221
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3769
14.5%
s 3209
12.4%
r 2384
 
9.2%
i 2254
 
8.7%
a 1489
 
5.7%
h 1278
 
4.9%
F 1236
 
4.8%
1135
 
4.4%
g 1125
 
4.3%
n 810
 
3.1%
Other values (18) 7221
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3769
14.5%
s 3209
12.4%
r 2384
 
9.2%
i 2254
 
8.7%
a 1489
 
5.7%
h 1278
 
4.9%
F 1236
 
4.8%
1135
 
4.4%
g 1125
 
4.3%
n 810
 
3.1%
Other values (18) 7221
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3769
14.5%
s 3209
12.4%
r 2384
 
9.2%
i 2254
 
8.7%
a 1489
 
5.7%
h 1278
 
4.9%
F 1236
 
4.8%
1135
 
4.4%
g 1125
 
4.3%
n 810
 
3.1%
Other values (18) 7221
27.9%

Price
Real number (ℝ)

Skewed  Zeros 

Distinct351
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean556090.5
Minimum0
Maximum4.4444435 × 108
Zeros150
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-11-12T03:20:49.956907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1149.99
median499.99
Q3999.99
95-th percentile1999.99
Maximum4.4444435 × 108
Range4.4444435 × 108
Interquartile range (IQR)850

Descriptive statistics

Standard deviation15474768
Coefficient of variation (CV)27.827787
Kurtosis819.93745
Mean556090.5
Median Absolute Deviation (MAD)399.99
Skewness28.64401
Sum1.376324 × 109
Variance2.3946845 × 1014
MonotonicityNot monotonic
2024-11-12T03:20:50.094923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999.99 346
 
14.0%
599.99 279
 
11.3%
299.99 153
 
6.2%
0 150
 
6.1%
199.99 136
 
5.5%
499.99 85
 
3.4%
99.99 54
 
2.2%
399.99 54
 
2.2%
249.99 46
 
1.9%
1000 41
 
1.7%
Other values (341) 1131
45.7%
ValueCountFrequency (%)
0 150
6.1%
0.01 1
 
< 0.1%
2 3
 
0.1%
2.1 1
 
< 0.1%
2.2 2
 
0.1%
2.3 1
 
< 0.1%
2.53 1
 
< 0.1%
2.79 1
 
< 0.1%
3 2
 
0.1%
3.33 1
 
< 0.1%
ValueCountFrequency (%)
444444346 1
< 0.1%
444443886 1
< 0.1%
444441110 1
< 0.1%
21000000 1
< 0.1%
12345678 1
< 0.1%
3768985 1
< 0.1%
3687772 1
< 0.1%
89999 1
< 0.1%
71700 1
< 0.1%
63003.5 1
< 0.1%

StockQuantity
Real number (ℝ)

Distinct497
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.85657
Minimum0
Maximum500
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-11-12T03:20:50.237395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q1118
median245
Q3366
95-th percentile472
Maximum500
Range500
Interquartile range (IQR)248

Descriptive statistics

Standard deviation143.49839
Coefficient of variation (CV)0.5860508
Kurtosis-1.1985564
Mean244.85657
Median Absolute Deviation (MAD)124
Skewness0.039653311
Sum606020
Variance20591.787
MonotonicityNot monotonic
2024-11-12T03:20:50.403542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140 14
 
0.6%
71 13
 
0.5%
93 12
 
0.5%
1 12
 
0.5%
496 11
 
0.4%
486 11
 
0.4%
168 11
 
0.4%
57 11
 
0.4%
143 11
 
0.4%
47 11
 
0.4%
Other values (487) 2358
95.3%
ValueCountFrequency (%)
0 3
 
0.1%
1 12
0.5%
2 6
0.2%
3 5
0.2%
4 3
 
0.1%
5 6
0.2%
6 5
0.2%
7 2
 
0.1%
8 6
0.2%
10 3
 
0.1%
ValueCountFrequency (%)
500 2
 
0.1%
499 1
 
< 0.1%
498 7
0.3%
497 2
 
0.1%
496 11
0.4%
495 3
 
0.1%
494 4
 
0.2%
493 4
 
0.2%
492 5
0.2%
491 7
0.3%
Distinct2447
Distinct (%)99.3%
Missing10
Missing (%)0.4%
Memory size19.5 KiB
2024-11-12T03:20:50.695049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length381
Median length215
Mean length145.95497
Min length20

Characters and Unicode

Total characters359779
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2430 ?
Unique (%)98.6%

Sample

1st rowThe Bosch Serie 4 Kil22VF30G Integrated Fridge features advanced temperature control and humidity management to provide optimal storage conditions for food, with a sleek and compact design that integrates seamlessly into any kitchen.
2nd rowThis Sony KD75XF8596BU 75“ 4K HDR Ultra HD Smart Android LED TV features integrated Amazon Alexa for seamless voice control and entertainment experiences.
3rd rowThe Sony Xperia XA2 Ultra Black features a 6-inch Full HD display, 32GB of internal storage, and supports 4G connectivity for seamless mobile experience.
4th rowThe Pentax K-1 Body is a weather-sealed digital single-lens reflex camera featuring an 24-70mm f/2.8 ED SDM WR zoom lens.
5th rowThis Samsung UE75MU7000TXXU is a 75-inch 7-Series LED Smart TV featuring a sleek black and silver design, equipped with advanced technologies for immersive viewing experiences.
ValueCountFrequency (%)
a 3386
 
6.4%
and 2619
 
5.0%
for 1612
 
3.1%
with 1121
 
2.1%
this 1104
 
2.1%
features 1022
 
1.9%
the 1006
 
1.9%
is 1004
 
1.9%
featuring 676
 
1.3%
advanced 616
 
1.2%
Other values (5064) 38683
73.2%
2024-11-12T03:20:51.146372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50385
14.0%
e 33089
 
9.2%
i 24654
 
6.9%
a 24075
 
6.7%
n 20022
 
5.6%
r 19066
 
5.3%
t 17994
 
5.0%
o 16445
 
4.6%
s 16291
 
4.5%
c 12921
 
3.6%
Other values (82) 124837
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 359779
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
50385
14.0%
e 33089
 
9.2%
i 24654
 
6.9%
a 24075
 
6.7%
n 20022
 
5.6%
r 19066
 
5.3%
t 17994
 
5.0%
o 16445
 
4.6%
s 16291
 
4.5%
c 12921
 
3.6%
Other values (82) 124837
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 359779
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
50385
14.0%
e 33089
 
9.2%
i 24654
 
6.9%
a 24075
 
6.7%
n 20022
 
5.6%
r 19066
 
5.3%
t 17994
 
5.0%
o 16445
 
4.6%
s 16291
 
4.5%
c 12921
 
3.6%
Other values (82) 124837
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 359779
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
50385
14.0%
e 33089
 
9.2%
i 24654
 
6.9%
a 24075
 
6.7%
n 20022
 
5.6%
r 19066
 
5.3%
t 17994
 
5.0%
o 16445
 
4.6%
s 16291
 
4.5%
c 12921
 
3.6%
Other values (82) 124837
34.7%

Rating
Real number (ℝ)

Distinct31
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5008485
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-11-12T03:20:51.272473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.75
median3.5
Q34.3
95-th percentile4.8
Maximum5
Range3
Interquartile range (IQR)1.55

Descriptive statistics

Standard deviation0.87606921
Coefficient of variation (CV)0.25024482
Kurtosis-1.2217046
Mean3.5008485
Median Absolute Deviation (MAD)0.8
Skewness0.0099629767
Sum8664.6
Variance0.76749726
MonotonicityNot monotonic
2024-11-12T03:20:51.399289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4.7 102
 
4.1%
2.8 102
 
4.1%
2.4 101
 
4.1%
3.5 96
 
3.9%
4.8 94
 
3.8%
3.6 90
 
3.6%
2.2 89
 
3.6%
2.1 89
 
3.6%
4.6 88
 
3.6%
4 88
 
3.6%
Other values (21) 1536
62.1%
ValueCountFrequency (%)
2 39
 
1.6%
2.1 89
3.6%
2.2 89
3.6%
2.3 65
2.6%
2.4 101
4.1%
2.5 79
3.2%
2.6 80
3.2%
2.7 77
3.1%
2.8 102
4.1%
2.9 79
3.2%
ValueCountFrequency (%)
5 33
 
1.3%
4.9 87
3.5%
4.8 94
3.8%
4.7 102
4.1%
4.6 88
3.6%
4.5 84
3.4%
4.4 83
3.4%
4.3 57
2.3%
4.2 82
3.3%
4.1 72
2.9%

Interactions

2024-11-12T03:20:45.931551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:41.780072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.473096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:43.245395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:43.910576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.594215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:45.252276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:46.029645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:41.874941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.577184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:43.337913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.002705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.682250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:45.343167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:46.138562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:41.976440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.679443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:43.444912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.107805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.784978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:45.445441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:46.236601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.068284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.779014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:43.534602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.198185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.870017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:45.535336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:46.337595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.161407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.886611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:43.627978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.289844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.963702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:45.630468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:46.432893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.252327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.986485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:43.720422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.389671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:45.054339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:45.728143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:46.529825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:42.348074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:43.092573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:43.811570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:44.489828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:45.148850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-12T03:20:45.822446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-12T03:20:51.493300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CategoryCategory IDCluster IDMerchant IDPriceProduct IDRatingStockQuantity
Category1.0001.0000.9330.2860.0000.8910.0200.000
Category ID1.0001.0000.9940.4300.0860.9940.021-0.021
Cluster ID0.9330.9941.0000.4470.0751.0000.019-0.020
Merchant ID0.2860.4300.4471.0000.0500.447-0.0020.012
Price0.0000.0860.0750.0501.0000.0750.010-0.042
Product ID0.8910.9941.0000.4470.0751.0000.019-0.020
Rating0.0200.0210.019-0.0020.0100.0191.000-0.031
StockQuantity0.000-0.021-0.0200.012-0.042-0.020-0.0311.000

Missing values

2024-11-12T03:20:46.672634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-12T03:20:46.853530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Product IDProductNameMerchant IDCluster IDCluster LabelCategory IDCategoryPriceStockQuantityDescriptionRating
043860bosch serie 4 kil22vf30g integrated fridge746162Bosch KIL22VF30G Integrated2623Fridges999.99473The Bosch Serie 4 Kil22VF30G Integrated Fridge features advanced temperature control and humidity management to provide optimal storage conditions for food, with a sleek and compact design that integrates seamlessly into any kitchen.4.2
110748sony kd75xf8596bu 75 4k hdr ultra hd smart android led tv amazon alexa1284465Sony KD-75XF85962614TVs99.99335This Sony KD75XF8596BU 75“ 4K HDR Ultra HD Smart Android LED TV features integrated Amazon Alexa for seamless voice control and entertainment experiences.4.0
2429sony xperia xa2 ultra black 6 32gb 4g unlocked sim free625Sony Xperia XA2 Ultra 32GB2612Mobile Phones599.99490The Sony Xperia XA2 Ultra Black features a 6-inch Full HD display, 32GB of internal storage, and supports 4G connectivity for seamless mobile experience.4.7
323568pentax k 1 body hd 24 70mm f/2 8 ed sdm wr26439409Pentax K-1 + 24-70mm2617Digital Cameras999.99144The Pentax K-1 Body is a weather-sealed digital single-lens reflex camera featuring an 24-70mm f/2.8 ED SDM WR zoom lens.3.0
412520samsung ue75mu7000txxu 75 inch 7 series led smart tv black/silver34872Samsung UE75MU70002614TVs599.9935This Samsung UE75MU7000TXXU is a 75-inch 7-Series LED Smart TV featuring a sleek black and silver design, equipped with advanced technologies for immersive viewing experiences.2.6
542732smeg pgf64 4 62cm classic 4 burner ultra low profile gas hob17945425Hotpoint MTZ622NF Silver2622Fridge Freezers199.00128This Smeg PGF64 4 62cm classic 4 burner ultra low profile gas hob features a sleek design and efficient cooking capabilities, making it an ideal choice for home cooks who value style and performance.2.1
626798bosch sps46iw00g 45cm serie 4 dishwasher in white 9 place settings12840509Bosch SPS46IW00G White2619Dishwashers499.99261The Bosch SPS46IW00G is a high-efficiency, 9-place setting dishwasher with advanced features for quiet operation and energy efficiency.4.8
710760samsung ue49nu7500 49 curved ultra hd certified hdr smart 4k tv324467Samsung UE49NU75002614TVs599.99215This Samsung 49" Curved Ultra HD Certified HDR Smart 4K TV features cutting-edge display technology for an immersive viewing experience.3.2
821963panasonic lumix fz330 bridge camera black738931Panasonic Lumix DMC-FZ3302617Digital Cameras499.99223The Panasonic Lumix FZ330 is a compact bridge camera featuring a 20.1MP sensor, 25x optical zoom, and 4K video recording capabilities.2.8
91761lg k8 2017 smartphone 5 inch display 5mp front camera android 7.0 b18267LG K8 M200N2612Mobile Phones500.00165A compact Android smartphone with a 5-inch display, featuring a 5MP front camera and running on Android 7.0 operating system.2.6
Product IDProductNameMerchant IDCluster IDCluster LabelCategory IDCategoryPriceStockQuantityDescriptionRating
246528269montpellier dw1254s 12 place dishwasher 5 progs class a silver14740776Montpellier DW1254S Silver2619Dishwashers599.99148This Montpellier DW1254S 12-place dishwasher features five advanced wash programs, including Class A-rated sanitization and a sleek silver finish, designed to provide efficient and hygienic cleaning for families and households of all sizes.2.9
24664058sony ericsson k800i481787Sony Ericsson K800i2612Mobile Phones249.99402The Sony Ericsson K800i is a mobile phone featuring an integrated camera with a 0.3MP resolution, allowing users to capture digital photos on the go.4.0
246731183indesit iwdd7143s/iwdd7143s9841962Indesit IWDD7143S2620Washing Machines0.00489The Indesit IWDD7143S/IWDD7143S is a 7kg capacity, 8th generation Wi-Fi enabled washing machine featuring advanced steam cleaning technology and multiple wash cycle options.2.4
246838321liebherr ctp 2921 freestanding fridge freezer comfort29444150Liebherr CTP 2921 White2622Fridge Freezers999.99107The Liebherr CTP 2921 Freestanding Fridge Freezer Comfort features advanced temperature control and humidity management for optimal food storage.4.8
246913777wof processor amd ryzen 7 1700x 8 x 3.4 ghz octa c165851AMD Ryzen 7 1700X 3.4GHz Box2615CPUs999.00289A high-performance computing module utilizing the AMD Ryzen 7 1700X 8-core, 3.4 GHz octa-core processor.4.8
247041734318litre retro fridge freezer class a chilli red12344935Britannia Breeze Retro Red2622Fridge Freezers318.0097This 318litre retro-style fridge freezer features a Chilli Red Class A finish, perfect for adding a pop of colour to any kitchen.2.9
247129484beko dfn04c11 white full size 12 place dishwasher18041271Beko DFC04C10W White2619Dishwashers149.99456This Beko DFN04C11 White Full Size 12 Place Dishwasher features a high-capacity design and advanced cycle options for efficient cleaning of large families.2.7
247217665intel pentium dual core e5400 2.7ghz 800fsb socket 775 2mb 2x1mb l2 cache retail boxed processor1217675Intel Pentium Dual core E5400 2 7GHz Socket 775 800MHz Box2615CPUs39.99122The Intel Pentium Dual Core E5400 processor features a 2.7 GHz clock speed and 2 MB of L2 cache, packaged in a socket 775 mounting system.3.9
2473792iphone xs gold 5.8 64gb 4g unlocked sim free660Apple iPhone XS 64GB2612Mobile Phones599.99486A high-performance smartphone with 5.8-inch display and 64GB storage, featuring 4G connectivity and unlocked for global use.2.9
247437786bosch serie 4 integrated fridge freezer frost free in white3144086Bosch KIN86VF30G Integrated2622Fridge Freezers999.99122This Bosch Serie 4 integrated fridge freezer features a frost-free design and sleek, white finish, ideal for seamless integration into any kitchen decor.2.1